The last few years have witnessed many exciting developments in the area of affective computing, which involves systems that are capable of recognizing and analyzing the expression of human emotions. However, most of the affective computing systems are still research-grade endeavors that are typically not robust enough to handle the demands of real world applications.
A capability that is useful for many affective computing systems is being able to know a user's expected response to a stimulus. While there are currently systems for measuring user response to stimuli, they are usually inadequate when it comes to real world applications. The experimental data they collect is often generated in a controlled, virtually sterile, environment. In these laboratory-like settings, a small number of short experiments are conducted (typically less than an hour long), in which a user's reactions are measured to a set of pre-selected stimuli, such as pictures, video scenes, or music. The main drawback of the laboratory-collected data is that it is acquired over a short period of time, while the user is in a specific, controlled situation. However, in reality, a user's reaction to stimuli may vary dramatically depending on the situation the user is in, making the laboratory-collected data less useful. For example, a user's response while driving in busy traffic might be quite different from the user's response when relaxing at home, even if exposed to the very similar stimuli in both situations. Furthermore, in short experiments, a user's reaction can only be measured to a small number of stimuli, which is inadequate for creating an affective computing system for real world applications that may have to consider the effect of a wide range of stimuli from multiple sources.
Analyzing affective response data collected in real world scenarios poses new challenges that are not likely arise with data collected in controlled laboratory-like situations. For example, while in the laboratory the user's response is usually measured for a single stimulus at a time, in the real world, the user is simultaneously exposed to many stimuli of different types and originating from multiple sources. Another characteristic of data acquired in real world situations is that it is often incomplete. For instance, while the system may have good information regarding the stimuli the user is exposed to, it might not be able to get an accurate assessment of the user's response. This is especially true if the user's response is only available under certain conditions, for instance, when the user is facing a camera. Given the many challenges and complications involved in the real world domain, a system designed to accurately determine a user's expected response to stimuli in real world scenarios should take into account the added complexity intrinsic to this domain.